Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction

Quantitative structure–activity relationships (QSAR) methods are urgently needed for predicting ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties to select lead compounds for optimization at the early stage of drug discovery, and to screen drug candidates for clinical trials. Use of suitable QSAR models ultimately results in lesser time-cost and lower attrition rate during drug discovery and development. In the case of ADME/T parameters, drug metabolism is a key determinant of metabolic stability, drug–drug interactions, and drug toxicity. QSAR models for predicting drug metabolism have undergone significant advances recently. However, most of the models used lack sufficient interpretability and offer poor predictability for novel drugs. In this review, we describe some considerations to be taken into account by QSAR for modeling drug metabolism, such as the accuracy/consistency of the entire data set, representation and diversity of the training and test sets, and variable selection. We also describe some novel statistical techniques (ensemble methods, multivariate adaptive regression splines and graph machines), which are not yet used frequently to develop QSAR models for drug metabolism. Subsequently, rational recommendations for developing predictable and interpretable QSAR models are made. Finally, the recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction, including in vivo hepatic clearance, in vitro metabolic stability, inhibitors and substrates of cytochrome P450 families, are briefly summarized.     

Simultaneous measurement of drug metabolic stability and identification of metabolites using ion-trap mass spectrometry

The use of in vitro drug metabolism data in the understanding of in vivo pharmacokinetic, safety and toxicity data has become a large area of scientific interest. This has stemmed from a trend in the pharmaceutical industry to use in vitro data generated from human tissue as a criterion to select compounds for further investigation. As well as measuring metabolic stability in vitro using human liver microsomal preparations, the identification of possible metabolite(s) formed may play a vital role in Hit-to-Lead and Lead optimisation processes. The data-dependent scan function mode with the ion-trap instrumentation provides the ability to measure the metabolic stability and identification of possible metabolites of a compound. A gradient liquid chromatographic method with a run time of 6 min/injection was developed for this purpose. The approach of simultaneous metabolic stability measurements and rapid identification of metabolites of drugs with high (verapamil), medium (propranolol and cisapride) and low (flunarazine) metabolic stabilities using ion-trap mass spectrometry is described. The metabolites identified after 15 min incubation for verapamil, propranolol and cisapride are in good agreement with those reported as the major metabolites in human in vivo studies.     

Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.     

Automatic mass spectrometry method development for drug discovery: application in metabolic stability assays

High-throughput metabolic screening has been requested routinely to keep pace with high-throughput organic synthesis. Liquid chromatography/tandem mass spectrometry (LC/MS/MS) with a fast gradient has become the method of choice for the task due to its sensitivity and selectivity. We have developed an automated system that consists of a robotic system for in vitro incubation and a commercially available software package for automatic MS/MS method development. A short, generic LC gradient and MS conditions that are applicable to most compounds have been developed to minimize the method development time and data analysis. This system has been used to support a number of in vitro screening assays in early drug discovery phase including microsomal stability and protein binding.     

In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery

Drug metabolism can have profound effects on the pharmacological and toxicological profile of therapeutic agents. In the pharmaceutical industry, many in vitro techniques are in place or under development to screen and optimize compounds for favorable metabolic properties in the drug discovery phase. These in vitro technologies are meant to address important issues such as: (1) is the compound a potent inhibitor of drug metabolising enzymes (DMEs)? (2) does the compound induce the expression of DMEs? (3) how labile is the compound to metabolic degradation? (4) which specific enzyme(s) is responsible for the compound's biotransformation? and (5) to which metabolites is the compound metabolized? Answers to these questions provide a basis for judging whether a compound is likely to have acceptable pharmacokinetic properties in vivo. To address these issues on the increasing number of compounds inundating the drug discovery programs, high throughput assays are essential. A combination of biochemical advances in the understanding of the function and regulation of DMEs (in particular, cytochromes P450, CYPs) and automated analytical technologies are revolutionizing drug metabolism research. Automated LC-MS based metabolic stability, fluorescence, radiometric and LC-MS based CYP inhibition assays are now in routine use. Automatible models for studying CYP induction based on enzyme activity, quantitative RT-PCR and reporter gene systems are being developed. We will review the utility and limitations of these HTS approaches and highlight on-going developments and emerging technologies to answer metabolism questions at the different stages of the drug discovery process.     

Development of QSAR models for microsomal stability: identification of good and bad structural features for rat,human and mouse microsomal stability

High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Naïve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.     

Improvement of "hit-to-lead" optimization by integration of in vitro HTS experimental models for early determination of pharmacokinetic properties

Development of predictive in vitro surrogate methods for traditional approaches assessing bioavailability and pharmacokinetics of lead compounds must be made to both keep pace with high-throughput (HT) lead identification and to mitigate the high costs associated with progression of compounds with poor chances of developmental success. Indeed opportunities for improvement still exist in the lead optimization phase versus the lead identification phase, where HT methodologies have been nearly optimized. Review of examples, limitations, and development of high-throughput microtiterplate-based assays for evaluating metabolic liabilities, such as in vitro radiometric and fluorometric assays for inhibition of cytochrome p450 (CYP) activity, determination of stability of a compound in liver microsomes, or cloned CYPs coupled to reconstituting systems are described. Parallel approaches to improve speed, resolution, sample preparation, as well as data analysis using LC/MS and LC/MS/MS approaches and technologies to assess compound integrity and biotransformation by automation and multiplexing are also discussed. Realization of the benefits in automation of cell-based models for determining drug permeability to predict drug absorption are still hampered by bottlenecks in analytical analysis of compounds. The implementation and limitations of surrogate physiochemical methods for passive adsorption such as immobilized artificial membranes (IAM) and parallel artificial membrane permeation assays (PAMPA), and compound solubility by laser nephelometry are reviewed as well. Additionally, data from a high-throughput 96-well equilibrium dialysis device, showing good correlation to classical methods, is presented. Finally, the impact of improvements in these downstream bottlenecks in lead optimization and preclinical drug discovery are discussed in this review.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

A computer program for automated data evaluation to support in vitro higher-throughput screening for drug metabolism and pharmacokinetics attributes

With the advances in analytical techniques, higher-throughput screening for drug metabolism and pharmacokinetics (DMPK) attributes has become an integral part of drug discovery. However, as the number of compounds increases, the volume of data that needs to be processed and evaluated increases exponentially. As a result, a major challenge for the analytical chemist is how to quickly process the vast amount of data so as to keep up with the throughput of the screening assay. We have developed a customized computer program for automated evaluation of the liquid chromatography/tandem mass spectrometric (LC/MS/MS) data generated from the in vitro DMPK screening assays. This program performs automatic data processing and quality control. It identifies analytical anomalies, such as low internal standard intensity and poor reproducibility of replicates. All analytical anomalies for individual compounds are summarized into an 'E-Log' in a color-coded format for reviewing. With the use of this program and other supporting software, data processing and evaluation for up to 100 compounds are accomplished in several minutes.     

The role of in vitro ADME assays in antimalarial drug discovery and development

The high level of attrition of drugs in clinical development has led pharmaceutical companies to increase the efficiency of their lead identification and development through techniques such as combinatorial chemistry and high-throughput (HTP) screening. Since the major reasons for clinical drug candidate failure other than efficacy are pharmacokinetics and toxicity, attention has been focused on assessing properties such as metabolic stability, drug-drug interactions (DDI), and absorption earlier in the drug discovery process. Animal studies are simply too labor-intensive and expensive to use for evaluating every hit, so it has been necessary to develop and implement higher throughput in vitro ADME screens to manage the large number of compounds of interest. The antimalarial drug development program at the Walter Reed Army Institute of Research, Division of Experimental Therapeutics (WRAIR/ET) has adopted this paradigm in its search for a long-term prophylactic for the prevention of malaria. The overarching goal of this program is to develop new, long half-life, orally bioavailable compounds with potent intrinsic activity against liver- and blood-stage parasites. From the WRAIR HTP antimalarial screen, numerous compounds are regularly identified with potent activity. These hits are now immediately evaluated using a panel of in vitro ADME screens to identify and predict compounds that will meet our specific treatment criteria. In this review, the WRAIR ADME screening program for antimalarial drugs is described as well as how we have implemented it to predict the ADME properties of small molecule for the identification of promising drug candidates.     

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less‐active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high‐throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high‐throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.     

A high throughput metabolic stability screening workflow with automated assessment of data quality in pharmaceutical industry

One of the most commonly performed in vitro ADME assays during the lead generation and lead optimization stage of drug discovery is metabolic stability evaluation. Metabolic stability is typically assessed in liver microsomes, which contain Phase I metabolizing enzymes, mainly cytochrome P450 enzymes (CYPs). The amount of parent drug metabolized by these CYPs is determined by LC/MS/MS. The metabolic stability data are typically used to rank order compounds for in vivo evaluation. We describe a streamlined and intelligent workflow for the metabolic stability assay that permits high throughput analyses to be carried out while maintaining the standard of high quality. This is accomplished in the following ways: a novel post-incubation pooling strategy based on c Log D_3.0 values, coupled with ultra-performance liquid chromatography/tandem mass spectrometry (UPLC/MS/MS), enables sample analysis times to be reduced significantly while ensuring adequate chromatographic separation of compounds within a group, so as to reduce the likelihood of compound interference. Assay quality and fast turnaround of data reports is ensured by performing automated real-time intelligent re-analysis of discrete samples for compounds that do not pass user-definable criteria during the pooling analysis. Intelligent, user-independent data acquisition and data evaluation are accomplished via a custom visual basic program that ties together every step in the workflow, including cassette compound selection, compound incubation, compound optimization, sample analysis and re-analysis (when appropriate), data processing, data quality evaluation, and database upload. The workflow greatly reduces labor and improves data turnaround time while maintaining high data quality.     

Liquid chromatography/tandem mass spectrometric quantification with metabolite screening as a strategy to enhance the early drug discovery process

Throughput for early discovery drug metabolism studies can be increased with the concomitant acquisition of metabolite screening information and quantitative analysis using ultra-fast gradient chromatographic methods. Typical ultra-fast high-performance liquid chromatography (HPLC) parameters used during early discovery pharmacokinetic (PK) studies, for example, employ full-linear gradients over 1-2 min at very high flow rates (1.5-2 mL/min) on very short HPLC columns (2 x 20 mm). These conditions increase sample throughput by reducing analytical run time without sacrificing chromatographic integrity and may be used to analyze samples generated from a variety of in vitro and in vivo studies. This approach allows acquisition of more information about a lead candidate while maintaining rapid analytical turn-around time. Some examples of this approach are discussed in further detail.     

In-silico investigation of phenolic compounds from leaves of Phillyrea angustifolia L. as a potential inhibitor against the SARS-CoV-2 main protease (Mpro PDB ID:5R83) using a virtual screening method

There is currently a global COVID-19 pandemic caused by the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) and its variants. This highly contagious viral disease continues to pose a major health threat global. The discovery of vaccinations is not enough to prevent their spread and dire consequences. To take advantage of the current drugs and isolated compounds, and immediately qualifying approach is required. The aim of our research is evaluation the potency for natural antiviral compounds against the SARS CoV-2 M^pro. Molecular docking of four phenolic compounds from Phillyrea angustifolia leaves with SARS-CoV-2 M^pro has been conducted. Similarly, the stability of selected ligand–protein interactions has been determined using MD simulations. Moreover, the quantitative structure–activity relationship (QSAR), MMGBSA binding energies, pharmacokinetics, and drug-likeness predictions for selected phenolic have been reported. The selected phenolic compounds (Luteolin-7-O-glucoside, Apigenin-7-O-glucoside, Demethyl-oleuropein, and Oleuropein aglycone) revealed strong binding contacts in the two active pockets of a target protein of SARS-CoV-2 M^pro with the docking scores and highest binding energies with a binding energy of ?8.2 kcal/mol; ?7.8 kcal/mol; ?7.2 kcal/mol and ?7.0 kcal/mol respectively. Both Demethyloleoeuropein and Oleuropein aglycone can interact with residues His41 and Cys145 (catalytic dyad) and other amino acids of the binding pocket of M^pro. According to QSAR, studies on pharmacokinetics and drug-like properties suggested that oleuropein aglycone could be the best inhibitor of SARS-CoV-2 for new drug design and development. Further in vivo, in vitro, and clinical studies are highly needed to examine the potential of these phenolic compounds in the fight against COVID-19.     

Fast mass spectrometry-based methodologies for pharmaceutical analyses

Historically, most bioanalytical methods for drug analysis in pharmaceutical industry were developed using HPLC coupled with UV or fluorescence detection. However, there is a trend toward interfacing separation technologies with more sensitive tandem mass spectrometry (MS/MS)-based systems. MS/MS detection offers complete resolution of the parent compounds from their first pass metabolites to avoid extra efforts for separation and sample clean-up procedures resulting in shorter run times. With the increasing demand for ever faster screening, there is a continuing demand for bioanalytical methods possessing higher sample throughput for both in vitro and in vivo drug metabolism and pharmacokinetic evaluations to accelerate the discovery process. This review focuses on the current approaches for fast MS-based assays (cycle-time less than 5 min) of pharmaceuticals and their metabolites that have been reported in the peer-reviewed publications.     

High-throughput cytochrome P450 inhibition screening via cassette probe-dosing strategy. IV. Validation of a direct injection on-line guard cartridge extraction/tandem mass spectrometry method for simultaneous CYP3A4, 2D6 and 2E1 inhibition assessment

A highly efficient direct injection on-line guard cartridge extraction/tandem mass spectrometry (DI-GCE/MS/MS) method has been validated for high-throughput evaluation of cytochrome P450 (CYP) 3A4, 2D6 and 2E1 inhibition potential via cassette dosing of midazolam, dextromethorphan and chlorzoxazone using human hepatic microsomes and 96-well microtiter plates. Microsomal incubations were terminated with formic acid, centrifuged, and the resulting supernatants were injected for analysis by DI-GCE/MS/MS. Due to the novel use of an extremely short C(18) guard cartridge (4 mm in length), this method exhibits several advantages such as no sample preparation, excellent on-line extraction, short run time (2.5 min), and minimized source contamination and performance deterioration. The DI-GCE/MS/MS method demonstrates acceptable accuracy and precision for the simultaneous quantification of 1'-hydroxymidazolam, dextrorphan and 6-hydroxychlorzoxazone in microsomal incubations. The inhibition potential of CYP3A4, 2D6 and 2E1 has been evaluated using their known selective inhibitors. The IC(50) values measured by the cassette dosing approach (high-throughput) are consistent with those observed by an individual dosing regimen (conventional) and are all in good agreement with the literature values. The results suggest that the cassette probe-dosing strategy may provide an in vitro approach to minimize cost while maximizing throughput of CYP inhibition evaluation of new chemical entities in support of drug discovery and development.     

High-throughput cytochrome p450 inhibition assays by ultrafast gradient liquid chromatography with tandem mass spectrometry using monolithic columns

A generic method employing ultrafast liquid chromatography with tandem mass spectrometry (LC/MS/MS) was developed and employed for routine screening of drug candidates for inhibition of five major human cytochrome p450 (CYP) isozymes, CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2. The method utilized a monolithic silica rod column to allow fast flow rates to significantly reduce chromatographic run time. The major metabolites of six CYP-specific probe substrates for the five p450 isoforms were monitored and quantified to determine IC(50) values of five drug compounds against each p450 isozyme. Human liver microsomal incubation samples at each test compound concentration were combined and analyzed simultaneously by the LC/MS/MS method. Each pooled sample containing six substrates and an internal standard was separated and detected in only 24 seconds. The combination of ultrafast chromatography and sample pooling techniques has significantly increased sample throughput and shortened assay turnaround time, allowing a large number of compounds to be screened rapidly for potential p450 inhibitory activity, to aid in compound selection and optimization in drug discovery.     

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.